Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for modeling transient current of a partially simulated hierarchical gate-level electronic design comprising: providing, using one or more processors, a partially simulated hierarchical gate-level electronic design, wherein the design includes a design hierarchy having one or more leaf blocks associated therewith; identifying activity of one or more sequential elements of the one or more leaf blocks using one or more simulation vectors, wherein the activity is used to estimate an amount of current associated with the one or more sequential elements; computing an adaptive activity of a parent block of the one or more leaf blocks, wherein the adaptive activity of the parent block corresponds to a weighted average of known activity of one or more leaf blocks; generating an adaptive activity of a top block of the one or more leaf blocks, based upon, at least in part, the adaptive activity of the parent block; and performing a mixed-mode simulation based upon, at least in part, the adaptive activity of the top block.
The invention relates to modeling transient current in partially simulated hierarchical gate-level electronic designs, addressing the challenge of accurately estimating power consumption in large-scale integrated circuits during simulation. Traditional methods struggle with computational efficiency when simulating entire designs at the gate level, particularly for transient current analysis. This method improves efficiency by selectively simulating only certain parts of the design while estimating current for the remaining components. The process begins by providing a partially simulated hierarchical gate-level electronic design, where the design includes a hierarchy of blocks, some of which are leaf blocks (lowest-level components). Simulation vectors are applied to identify the activity of sequential elements (e.g., flip-flops) within these leaf blocks, which helps estimate the current consumption of those elements. For higher-level parent blocks, an adaptive activity metric is computed as a weighted average of the known activity of their child leaf blocks. This adaptive activity is then propagated upward through the hierarchy to generate an adaptive activity for the top-level block. Finally, a mixed-mode simulation is performed, combining detailed simulation of some blocks with estimated current values for others, based on the adaptive activity of the top block. This approach reduces computational overhead while maintaining accuracy in transient current modeling.
2. The computer-implemented method of claim 1 , wherein generating an adaptive activity of a top block of the one or more leaf blocks includes hierarchically computing activity from a bottom-most level up through the design hierarchy until determining the adaptive activity of the top block.
This invention relates to computer-implemented methods for analyzing and optimizing electronic circuit designs, particularly in hierarchical design structures. The problem addressed is the need for efficient computation of adaptive activities (e.g., signal switching probabilities) across multiple levels of a design hierarchy, which is critical for power estimation, timing analysis, and other design optimizations. The method involves a hierarchical approach to compute adaptive activities starting from the lowest (bottom-most) level of the design and propagating the results upward through the design hierarchy. This bottom-up computation ensures that activities are accurately determined for each block, including leaf blocks (the smallest functional units) and higher-level blocks. The process continues until the adaptive activity of the top block (the highest level in the hierarchy) is determined. This approach improves accuracy and efficiency compared to traditional methods that may rely on less systematic or non-hierarchical computations. The technique is particularly useful in integrated circuit design, where understanding signal activity at different levels of abstraction is essential for performance and power optimization. The method may be implemented in electronic design automation (EDA) tools to support design verification, optimization, and validation processes.
3. The computer-implemented method of claim 1 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a sum of a number of block instances and a block activity level.
This invention relates to adaptive activity management in computer systems, specifically for dynamically adjusting the activity levels of hierarchical blocks based on usage patterns. The problem addressed is the inefficient allocation of computational resources in systems where certain blocks may be overutilized while others remain underutilized, leading to suboptimal performance. The method involves monitoring and adjusting the adaptive activity of hierarchical blocks, such as top-level or parent blocks, within a system. The adaptive activity is determined by a combination of two factors: the number of block instances and the block activity level. The number of block instances refers to how frequently or how many times a particular block is instantiated or used within the system. The block activity level represents the intensity or frequency of operations performed by the block. By summing these two factors, the system can dynamically adjust the activity level of a block to optimize resource allocation. For example, if a block has a high number of instances but a low activity level, the system may reduce its adaptive activity to prevent unnecessary resource consumption. Conversely, if a block has a low number of instances but a high activity level, the system may increase its adaptive activity to ensure sufficient resources are available. This adaptive approach allows the system to balance resource allocation based on real-time usage patterns, improving overall efficiency and performance. The method can be applied in various computing environments, including distributed systems, cloud computing, and real-time processing systems.
4. The computer-implemented method of claim 3 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a list of blocks having known activity levels.
This invention relates to a computer-implemented method for managing adaptive activities in a hierarchical block structure, such as in a data processing or computational system. The problem addressed is the need to dynamically adjust the activity levels of blocks within a hierarchy to optimize performance, efficiency, or resource allocation. The method involves determining the adaptive activity of a top block or a parent block based, at least in part, on a predefined list of blocks with known activity levels. This list serves as a reference to guide the adjustment of activity levels, ensuring that the system can adapt to changing conditions or requirements. The adaptive activity may involve modifying computational tasks, power states, or other operational parameters of the blocks. The method leverages the hierarchical relationship between blocks, where a parent block influences the activity of its child blocks, and the top block may represent the highest level in the hierarchy. The use of known activity levels allows for predictable and controlled adjustments, improving system stability and performance. This approach is particularly useful in systems where dynamic adaptation is necessary to handle varying workloads or environmental conditions.
5. The computer-implemented method of claim 1 , wherein the one or more leaf blocks include at least one block having a known activity level, at least one block having an unknown activity level, and at least one block having a calculated activity level.
This invention relates to a computer-implemented method for analyzing activity levels within a data structure, specifically focusing on a hierarchical organization of data blocks. The method addresses the challenge of efficiently tracking and assessing activity levels across different segments of a data system, particularly in scenarios where some blocks have known activity levels, others have unknown activity levels, and additional blocks require calculated activity levels. The method involves processing a hierarchical data structure where data is organized into leaf blocks. These leaf blocks are categorized into three distinct types: blocks with known activity levels, blocks with unknown activity levels, and blocks with calculated activity levels. The known activity level blocks have predefined or previously determined activity metrics, while the unknown activity level blocks lack such metrics and require further evaluation. The calculated activity level blocks are derived through computational analysis, where activity levels are determined based on available data or inferred from related blocks. The method ensures comprehensive activity tracking by incorporating all three types of blocks, allowing for a complete assessment of system activity. This approach is particularly useful in data management systems where activity monitoring is critical for performance optimization, security analysis, or resource allocation. By distinguishing between known, unknown, and calculated activity levels, the method provides a flexible and adaptive framework for handling diverse data scenarios.
6. The computer-implemented method of claim 5 , wherein generating the adaptive activity of the top block of the one or more leaf blocks is based upon, at least in part, the adaptive activity of a plurality of parent blocks.
This invention relates to adaptive activity generation in hierarchical block structures, such as those used in computational models or data processing systems. The problem addressed is efficiently determining the behavior of a top block in a hierarchical structure based on the activities of its parent blocks, ensuring consistency and coherence across different levels of the hierarchy. The method involves generating adaptive activity for a top block within a hierarchical structure composed of multiple leaf blocks. The adaptive activity of the top block is derived, at least in part, from the adaptive activities of a plurality of parent blocks. These parent blocks are positioned at a higher level in the hierarchy and influence the behavior of the top block. The method ensures that the top block's activity is dynamically adjusted based on the aggregated or processed activities of its parent blocks, allowing for real-time or context-aware adaptations. The hierarchical structure may include multiple layers, where each block at a given level influences blocks at lower levels. The adaptive activity generation process may involve aggregating, filtering, or otherwise processing the activities of the parent blocks to determine the appropriate behavior for the top block. This approach enables scalable and efficient adaptation in systems where hierarchical relationships are critical, such as in computational models, decision-making frameworks, or data processing pipelines. The method ensures that changes in parent block activities propagate appropriately to the top block, maintaining system coherence and responsiveness.
7. The computer-implemented method of claim 1 , wherein performing a mixed-mode simulation includes a vector based analysis method and a vector-less analysis method.
This invention relates to computer-implemented methods for performing mixed-mode simulations in electronic circuit design. The method addresses the challenge of efficiently analyzing circuits that include both digital and analog components, where traditional simulation approaches may be computationally expensive or inaccurate. The mixed-mode simulation combines a vector-based analysis method with a vector-less analysis method to improve simulation accuracy and performance. The vector-based analysis method processes signals as discrete vectors, capturing precise timing and amplitude information, while the vector-less analysis method approximates signal behavior without explicit vector representations, reducing computational overhead. The method dynamically selects or integrates these approaches based on circuit characteristics, such as signal types or complexity, to optimize simulation efficiency. This hybrid approach ensures accurate modeling of analog behavior while maintaining computational feasibility for large-scale digital circuits. The invention is particularly useful in verifying mixed-signal integrated circuits, where both analog and digital interactions must be accurately simulated. By leveraging both vector-based and vector-less techniques, the method balances precision and performance, enabling faster design iterations and reducing simulation costs.
8. A system for modeling transient current of a partially simulated hierarchical gate-level electronic design comprising: a computing device having at least one processor configured to receive, using one or more processors, a partially simulated hierarchical gate-level electronic design, wherein the design includes a design hierarchy having one or more leaf blocks associated therewith, the at least one processor further configured to identify activity of one or more sequential elements of the one or more leaf blocks using one or more simulation vectors, wherein the activity is used to estimate an amount of current associated with the one or more sequential elements, the at least one processor further configured to compute an adaptive activity of a parent block of the one or more leaf blocks, wherein the adaptive activity of the parent block corresponds to a weighted average of known activity of one or more leaf blocks, the at least one processor further configured to generate an adaptive activity of a top block of the one or more leaf blocks, based upon, at least in part, the adaptive activity of the parent block, the at least one processor further configured to perform a mixed-mode simulation based upon, at least in part, the adaptive activity of the top block.
The system models transient current in partially simulated hierarchical gate-level electronic designs, addressing the challenge of accurately estimating power consumption in large-scale integrated circuits during simulation. The design includes a hierarchy of blocks, where leaf blocks are the lowest-level components. The system identifies the activity of sequential elements (e.g., flip-flops) within these leaf blocks using simulation vectors, which helps estimate the current consumption of these elements. For parent blocks (higher-level blocks in the hierarchy), the system computes an adaptive activity as a weighted average of the known activity of their child leaf blocks. This adaptive activity is then propagated upward to the top-level block, which represents the entire design. The system uses this hierarchical activity data to perform a mixed-mode simulation, combining detailed simulation of critical parts with approximate modeling of less critical sections. This approach reduces computational complexity while maintaining accuracy in power estimation, making it suitable for large-scale designs.
9. The system of claim 8 , wherein generating an adaptive activity of a top block of the one or more leaf blocks includes hierarchically computing activity from a bottom-most level up through the design hierarchy until determining the adaptive activity of the top block.
This invention relates to electronic design automation (EDA), specifically optimizing power analysis in integrated circuit (IC) designs. The problem addressed is the computational inefficiency of traditional power analysis methods, which often require exhaustive simulations or static analysis across entire designs, leading to excessive runtime and resource consumption. The system computes adaptive activity metrics for hierarchical IC designs by propagating activity data bottom-up through the design hierarchy. A top block's activity is determined by hierarchically aggregating activity from lower-level leaf blocks, starting from the bottom-most level and progressing upward. This approach avoids redundant calculations by focusing only on relevant design portions, significantly improving efficiency. The method supports dynamic adjustments based on design changes, ensuring accurate power estimates without full re-simulations. Key components include activity propagation logic, hierarchical design representation, and adaptive computation modules that prioritize critical design regions. The system integrates with existing EDA tools to enhance power analysis workflows, reducing time and computational overhead while maintaining accuracy. This technique is particularly valuable for large, complex IC designs where traditional methods are impractical.
10. The system of claim 8 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a sum of a number of block instances and a block activity level.
This invention relates to adaptive systems for managing computational blocks, particularly in distributed or modular computing environments. The problem addressed is optimizing resource allocation and performance by dynamically adjusting the activity of computational blocks based on their usage and demand. The system includes a hierarchical structure of blocks, where each block can be a parent or child in a nested arrangement. The adaptive activity of a top-level block or its parent block is determined by a combination of two factors: the number of block instances and the block activity level. The number of block instances refers to how many times the block is instantiated or replicated in the system, while the block activity level represents the current utilization or demand for the block's computational resources. By considering both factors, the system can dynamically scale or adjust the block's activity to balance load, improve efficiency, and prevent resource exhaustion. The adaptive mechanism ensures that blocks with higher demand or more instances receive appropriate priority, while underutilized blocks can be scaled back or deactivated. This approach enhances system responsiveness and resource management in environments where workloads vary over time. The invention is particularly useful in cloud computing, distributed systems, or any modular architecture requiring dynamic resource allocation.
11. The system of claim 10 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a list of blocks having known activity levels.
The invention relates to a system for managing adaptive activities within a hierarchical block structure, such as in a computing or data processing environment. The system addresses the challenge of dynamically adjusting the behavior of blocks in a hierarchy based on their activity levels, ensuring efficient resource allocation and performance optimization. The system includes a hierarchical arrangement of blocks, where each block can have adaptive activities that are adjusted based on its own activity level or the activity level of its parent block. The adaptive activity of a top block or a parent block is determined, at least in part, by referencing a predefined list of blocks that have known activity levels. This list serves as a reference to guide the adjustment of activities, allowing the system to make informed decisions about how to modify the behavior of blocks to improve overall system performance. By using the known activity levels of blocks, the system can dynamically adapt the activities of higher-level blocks, such as top or parent blocks, to optimize resource usage and ensure efficient operation. This approach helps in maintaining system stability and responsiveness, particularly in environments where block activity levels may vary over time. The system may also include mechanisms for updating the list of known activity levels to reflect changes in block behavior, ensuring that the adaptive activities remain accurate and effective.
12. The system of claim 11 , wherein generating the adaptive activity of the top block of the one or more leaf blocks is based upon, at least in part, the adaptive activity of a plurality of parent blocks.
This invention relates to a hierarchical system for generating adaptive activities within a block-based structure, particularly in computational or data processing environments. The system addresses the challenge of dynamically adjusting activities in a hierarchical arrangement where lower-level blocks (leaf blocks) must respond to changes in higher-level blocks (parent blocks). The system includes multiple leaf blocks, each capable of performing adaptive activities, and a mechanism to propagate and integrate the adaptive activities of parent blocks into the activities of the leaf blocks. Specifically, the adaptive activity of a top block among the leaf blocks is determined based, at least in part, on the adaptive activities of its parent blocks. This ensures that the system maintains coherence and responsiveness across different hierarchical levels, allowing for coordinated adjustments in complex, multi-layered structures. The system may be applied in fields such as distributed computing, machine learning, or real-time data processing, where hierarchical dependencies and adaptive behavior are critical. The invention improves efficiency and reliability by ensuring that lower-level activities are dynamically influenced by higher-level decisions, reducing inconsistencies and improving overall system performance.
13. The system of claim 8 , wherein the one or more leaf blocks include at least one block having a known activity level, at least one block having an unknown activity level, and at least one block having a calculated activity level.
This invention relates to a system for monitoring and analyzing activity levels within a distributed or hierarchical data structure, such as a blockchain or a tree-based system. The problem addressed is the need to efficiently track and assess activity levels across different segments of the structure, particularly when some segments have known activity levels, others have unknown activity levels, and still others require calculated activity levels based on available data. The system includes a hierarchical structure with one or more leaf blocks, where each leaf block represents a segment of the system. The leaf blocks are categorized into three types: blocks with known activity levels, blocks with unknown activity levels, and blocks with calculated activity levels. Known activity levels are directly measured or provided, while unknown activity levels lack sufficient data for direct measurement. Calculated activity levels are derived from available data, such as statistical models or inferred relationships with other blocks. The system dynamically processes these blocks to ensure accurate activity tracking. For blocks with unknown activity levels, the system may use interpolation, extrapolation, or machine learning techniques to estimate activity. For calculated activity levels, the system applies predefined algorithms or rules to compute the activity based on neighboring or related blocks. This approach ensures comprehensive monitoring while accommodating data gaps and uncertainties. The invention improves efficiency in activity monitoring by systematically categorizing and processing different types of activity data, enabling better decision-making and system optimization.
14. The system of claim 8 , wherein performing a mixed-mode simulation includes a vector based analysis method and a vector-less analysis method.
A system for electronic circuit simulation performs mixed-mode simulations to analyze both digital and analog behavior of circuits. The system combines vector-based and vector-less analysis methods to improve accuracy and efficiency. Vector-based analysis involves using detailed signal waveforms (vectors) to model circuit behavior, providing precise timing and voltage information. Vector-less analysis, on the other hand, abstracts signal behavior to reduce computational complexity while still capturing critical timing and functional interactions. By integrating both approaches, the system can efficiently simulate complex circuits that include both digital logic and analog components. This hybrid method reduces simulation time compared to purely vector-based methods while maintaining accuracy for critical analog effects. The system is particularly useful in integrated circuit design, where mixed-signal circuits require both high-fidelity analog modeling and fast digital verification. The mixed-mode simulation allows designers to verify functionality, timing, and power consumption across different circuit domains in a single simulation environment. This approach is beneficial for applications such as system-on-chip (SoC) design, where digital processors interact with analog interfaces like sensors and power management units. The system optimizes simulation performance by dynamically selecting analysis methods based on circuit requirements, ensuring efficient and accurate results.
15. A computer readable storage medium having stored thereon instructions, which when executed by a processor, result in one or more operations for modeling transient current of a partially simulated hierarchical gate-level electronic design, the operations comprising: providing, using one or more processors, a partially simulated hierarchical gate-level electronic design, wherein the design includes a design hierarchy having one or more leaf blocks associated therewith; identifying activity of one or more sequential elements of the one or more leaf blocks using one or more simulation vectors, wherein the activity is used to estimate an amount of current associated with the one or more sequential elements; computing an adaptive activity of a parent block of the one or more leaf blocks, wherein the adaptive activity of the parent block corresponds to a weighted average of known activity of one or more leaf blocks; generating an adaptive activity of a top block of the one or more leaf blocks, based upon, at least in part, the adaptive activity of the parent block; and performing a mixed-mode simulation based upon, at least in part, the adaptive activity of the top block.
This invention relates to modeling transient current in partially simulated hierarchical gate-level electronic designs, addressing the challenge of accurately estimating power consumption in large-scale integrated circuits during simulation. The method involves analyzing a hierarchical gate-level design, which includes multiple leaf blocks and their parent blocks, to compute transient current more efficiently than full simulation. Sequential elements within leaf blocks are first activated using simulation vectors to estimate their current contributions. The activity of these elements is then used to compute an adaptive activity level for parent blocks, which is a weighted average of known activity from the leaf blocks. This adaptive activity is propagated upward to generate a top-level activity estimate, which guides a mixed-mode simulation combining detailed and approximate modeling. The approach reduces computational overhead by focusing simulation efforts on active regions while maintaining accuracy in power estimation. This technique is particularly useful for verifying power integrity in complex digital circuits where full simulation is impractical.
16. The computer readable storage medium of claim 15 , wherein generating an adaptive activity of a top block of the one or more leaf blocks includes hierarchically computing activity from a bottom-most level up through the design hierarchy until determining the adaptive activity of the top block.
This invention relates to electronic design automation (EDA) for integrated circuits, specifically optimizing power analysis in hierarchical circuit designs. The problem addressed is the computational inefficiency of traditional power analysis methods, which often require full-chip simulations or redundant calculations across hierarchical design levels, leading to excessive processing time and resource consumption. The solution involves a hierarchical power analysis system that computes adaptive activity metrics for circuit blocks in a bottom-up manner. Starting from the lowest (leaf) blocks, the system calculates activity metrics and propagates them upward through the design hierarchy. This approach avoids redundant calculations by leveraging previously computed data from lower levels. The adaptive activity of a top-level block is determined by aggregating and refining activity data from all underlying blocks, ensuring accurate power estimation without reprocessing lower-level details. Key features include hierarchical computation, adaptive activity propagation, and efficient power estimation by minimizing redundant calculations. The method is particularly useful for large-scale integrated circuit designs where traditional flat simulations are impractical due to computational complexity. By focusing on incremental updates and hierarchical aggregation, the system significantly reduces processing time while maintaining accuracy in power analysis.
17. The computer readable storage medium of claim 15 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a sum of a number of block instances and a block activity level.
This invention relates to adaptive block management in computer systems, particularly for optimizing performance by dynamically adjusting block activities based on usage patterns. The technology addresses the problem of inefficient resource allocation in systems where static block configurations fail to adapt to varying workload demands, leading to suboptimal performance or resource waste. The invention involves a method for managing blocks in a computer system, where each block can be a top block or a parent block within a hierarchical structure. The adaptive activity of these blocks is determined by a combination of two factors: the number of block instances and the block activity level. The number of block instances refers to how many times the block is instantiated or used, while the block activity level represents the frequency or intensity of operations performed by the block. By summing these two metrics, the system dynamically adjusts the block's activity, ensuring that frequently used or highly active blocks receive higher priority or resources, while less active blocks are deprioritized or deallocated. This adaptive mechanism improves system efficiency by aligning resource allocation with actual usage patterns, reducing overhead and enhancing performance. The invention is particularly useful in environments where workloads fluctuate, such as cloud computing, virtualization, or real-time processing systems. The adaptive activity adjustment ensures that the system remains responsive and resource-efficient under varying conditions.
18. The computer readable storage medium of claim 17 , wherein the adaptive activity of the top block or the adaptive activity of the parent block is based upon, at least in part, a list of blocks having known activity levels.
This invention relates to adaptive data storage systems, specifically optimizing block activity in storage devices to improve performance and efficiency. The problem addressed is inefficient data management in storage systems, where static or poorly optimized block activity leads to degraded performance, increased wear, and reduced lifespan of storage media. The invention involves a computer-readable storage medium containing instructions for managing block activity in a storage system. The system includes a top block and one or more parent blocks, each with adaptive activity levels that adjust dynamically based on usage patterns. The adaptive activity of these blocks is determined, at least in part, by a predefined list of blocks with known activity levels. This list helps the system predict and optimize block usage, ensuring that frequently accessed blocks are prioritized while less active blocks are managed to reduce wear and improve efficiency. The system may also include mechanisms to track and update the activity levels of blocks over time, allowing the storage medium to adapt to changing usage patterns. By referencing the list of known activity levels, the system can make informed decisions about block allocation, data placement, and wear leveling, leading to more efficient storage operations. This approach enhances performance, extends the lifespan of storage media, and reduces energy consumption by minimizing unnecessary operations on inactive blocks.
19. The computer readable storage medium of claim 15 , wherein the one or more leaf blocks include at least one block having a known activity level, at least one block having an unknown activity level, and at least one block having a calculated activity level.
This invention relates to data storage systems, specifically optimizing storage efficiency and performance by managing activity levels of data blocks. The problem addressed is inefficient storage utilization and degraded performance due to improper handling of data blocks with varying access patterns. The solution involves categorizing data blocks into three distinct types based on their activity levels: known, unknown, and calculated. Known activity blocks are those with pre-determined or historically established access frequencies. Unknown activity blocks are those whose access patterns are not yet determined. Calculated activity blocks are those whose activity levels are derived through analysis, such as predictive algorithms or statistical methods. The system dynamically assigns blocks to these categories to optimize storage operations, such as tiering, garbage collection, or caching. By distinguishing between these activity levels, the system improves storage efficiency, reduces unnecessary data movement, and enhances overall system performance. The invention applies to various storage environments, including solid-state drives, databases, and distributed storage systems, where understanding and managing data activity is critical for performance optimization.
20. The computer readable storage medium of claim 15 , wherein performing a mixed-mode simulation includes a vector based analysis method and a vector-less analysis method.
A computer-readable storage medium stores instructions for performing mixed-mode simulations of electronic circuits, combining both vector-based and vector-less analysis methods. The system simulates interactions between analog and digital circuit components, where the vector-based analysis method processes signals with defined waveforms, while the vector-less analysis method handles signals without explicit waveform data. The simulation integrates these two approaches to model complex circuits where some components require precise signal representation and others can be approximated. The system dynamically switches between methods based on circuit requirements, optimizing accuracy and computational efficiency. This hybrid approach reduces simulation time while maintaining accuracy for critical signal paths. The method is particularly useful in integrated circuit design, where mixed-signal circuits combine analog and digital elements, and accurate simulation is essential for performance validation. The storage medium may include additional instructions for preprocessing circuit data, configuring simulation parameters, and post-processing results to generate performance metrics. The system supports real-time adjustments during simulation to refine accuracy or speed based on intermediate results. This technique addresses the challenge of efficiently simulating mixed-signal circuits, where traditional methods either lack precision or are computationally expensive.
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March 10, 2020
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